import os
# -----------------------数据的路径相关参数---------------------------------------

# data_root='E:/AllData/LITS17/'#本机路径
# project_root='E:/pythonProject/Graduation Design/My_LITS_seg/'
# Liver_or_Tumor="Tumor"#"Tumor" or "Liver" or "OnlyTumor"，分别代表（肝脏+肿瘤分割）、（肝脏分割）、（肿瘤分割）
# n_class=3#"Tumor:3" or "Liver:2" or "OnlyTumor:2"


n_class=2#"Tumor:3" or "Liver:2" or "OnlyTumor:2"用于评估函数
#GPU2地址
# project_root='/home/liukai/projects/TransFuseForBreast'
# data_root='/home/liukai/AllData/zunYiBreast/'
#GPU3地址
project_root="/data1/home/liukai/projects/TransFuseForBreast/"
data_root="/data1/home/liukai/AllData/zunYiBreast/"
#本机路径
# project_root=r'E:\postgraduate project\TransFuseForBreast'
# data_root=r"E:\AllData\zunYiBreast"

nii_volume_path=os.path.join(data_root,'nii/volume')
nii_seg_path=os.path.join(data_root,'nii/segmentation')
cut2d_save_path=os.path.join(data_root,'2Dcut')
cut2d_save_path_vol=os.path.join(cut2d_save_path,"volume")
cut2d_save_path_seg=os.path.join(cut2d_save_path,"segmentation")

test_nii_id_path =os.path.join(project_root, 'dataset/zunYi/testNii.txt')
train_nii_id_path=os.path.join(project_root, 'dataset/zunYi/trainNii.txt')


train2d_choose_id_path=os.path.join(project_root, 'dataset/zunYi/2d/train2dChoose.txt')
train2d_notchoose_id_path=os.path.join(project_root, 'dataset/zunYi/2d/train2dNotChoose.txt')
test2d_id_path=os.path.join(project_root, 'dataset/zunYi/2d/test2d.txt')

photo_save_path=os.path.join(data_root,'predict_photo')#预测的图片的保存位置
#-----------------模型训练的参数-----------------#
channel=1
train_batch_size=8
test_batch_size=1
epoch=100
learning_rate=8e-7
# learning_rate=0.01
grad_norm=2#torch.nn.utils.clip_grad_norm_(model.parameters(), para.grad_norm)
train_save='TransFuse_S'
#----------------#


